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A shrinkage-type variable selection procedure for varying coefficient models is routinely established in the least-squares (LS) framework. Although the LS method has favorable properties for a large class of error distributions, it will break down if the error variance is infinite and is adversely affected by outliers and heavy-tail distributions. To overcome these issues, we propose a robust shrinkage...
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